Quantitative chest computed tomography is associated with two prediction models of mortality in interstitial lung disease related to systemic sclerosis
In this multicentre study, we aimed to evaluate the capacity of a computer-assisted automated QCT method to identify patients with SSc-associated interstitial lung disease (SSc-ILD) with high mortality risk according to validated composite clinical indexes (ILD-Gender, Age, Physiology index and du B...
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Veröffentlicht in: | Rheumatology (Oxford, England) England), 2017-06, Vol.56 (6), p.922-927 |
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creator | Ariani, Alarico Silva, Mario Seletti, Valeria Bravi, Elena Saracco, Marta Parisi, Simone De Gennaro, Fabio Idolazzi, Luca Caramaschi, Paola Benini, Camilla Bodini, Flavio Cesare Scirè, Carlo Alberto Carrara, Greta Lumetti, Federica Alfieri, Veronica Bonati, Elisa Lucchini, Gianluca Aiello, Marina Santilli, Daniele Mozzani, Flavio Imberti, Davide Michieletti, Emanuele Arrigoni, Eugenio Delsante, Giovanni Pellerito, Raffaele Fusaro, Enrico Chetta, Alfredo Sverzellati, Nicola |
description | In this multicentre study, we aimed to evaluate the capacity of a computer-assisted automated QCT method to identify patients with SSc-associated interstitial lung disease (SSc-ILD) with high mortality risk according to validated composite clinical indexes (ILD-Gender, Age, Physiology index and du Bois index).
Chest CT, anamnestic data and pulmonary function tests of 146 patients with SSc were retrospectively collected, and the ILD-Gender, Age, Physiology score and DuBois index were calculated. Each chest CT underwent an operator-independent quantitative assessment performed with a free medical image viewer (Horos). The correlation between clinical prediction models and QCT parameters was tested. A value of P < 0.05 was considered statistically significant.
Most QCT parameters had a statistically different distribution in patients with diverging mortality risk according to both clinical prediction models (P < 0.01). The cut-offs of QCT parameters were calculated by receiver operating characteristic curve analysis, and most of them could discriminate patients with different mortality risk according to clinical prediction models.
QCT assessment of SSc-ILD can discriminate between well-defined different mortality risk categories, supporting its prognostic value. These findings, together with the operator independence, strengthen the validity and clinical usefulness of QCT for assessment of SSc-ILD. |
doi_str_mv | 10.1093/rheumatology/kew480 |
format | Article |
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Chest CT, anamnestic data and pulmonary function tests of 146 patients with SSc were retrospectively collected, and the ILD-Gender, Age, Physiology score and DuBois index were calculated. Each chest CT underwent an operator-independent quantitative assessment performed with a free medical image viewer (Horos). The correlation between clinical prediction models and QCT parameters was tested. A value of P < 0.05 was considered statistically significant.
Most QCT parameters had a statistically different distribution in patients with diverging mortality risk according to both clinical prediction models (P < 0.01). The cut-offs of QCT parameters were calculated by receiver operating characteristic curve analysis, and most of them could discriminate patients with different mortality risk according to clinical prediction models.
QCT assessment of SSc-ILD can discriminate between well-defined different mortality risk categories, supporting its prognostic value. These findings, together with the operator independence, strengthen the validity and clinical usefulness of QCT for assessment of SSc-ILD.</description><identifier>ISSN: 1462-0324</identifier><identifier>EISSN: 1462-0332</identifier><identifier>DOI: 10.1093/rheumatology/kew480</identifier><identifier>PMID: 28160007</identifier><language>eng</language><publisher>England</publisher><subject>Female ; Humans ; Italy - epidemiology ; Lung Diseases, Interstitial - diagnostic imaging ; Lung Diseases, Interstitial - mortality ; Male ; Middle Aged ; Models, Statistical ; Retrospective Studies ; Risk Assessment ; Scleroderma, Systemic - diagnostic imaging ; Scleroderma, Systemic - mortality ; Tomography, X-Ray Computed - mortality</subject><ispartof>Rheumatology (Oxford, England), 2017-06, Vol.56 (6), p.922-927</ispartof><rights>The Author 2017. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c350t-b817ff6c2c1756ec58fbd2f6eb4addb0d804c3e6f892f0c8d8948c2daa6285773</citedby><cites>FETCH-LOGICAL-c350t-b817ff6c2c1756ec58fbd2f6eb4addb0d804c3e6f892f0c8d8948c2daa6285773</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,27929,27930</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28160007$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ariani, Alarico</creatorcontrib><creatorcontrib>Silva, Mario</creatorcontrib><creatorcontrib>Seletti, Valeria</creatorcontrib><creatorcontrib>Bravi, Elena</creatorcontrib><creatorcontrib>Saracco, Marta</creatorcontrib><creatorcontrib>Parisi, Simone</creatorcontrib><creatorcontrib>De Gennaro, Fabio</creatorcontrib><creatorcontrib>Idolazzi, Luca</creatorcontrib><creatorcontrib>Caramaschi, Paola</creatorcontrib><creatorcontrib>Benini, Camilla</creatorcontrib><creatorcontrib>Bodini, Flavio Cesare</creatorcontrib><creatorcontrib>Scirè, Carlo Alberto</creatorcontrib><creatorcontrib>Carrara, Greta</creatorcontrib><creatorcontrib>Lumetti, Federica</creatorcontrib><creatorcontrib>Alfieri, Veronica</creatorcontrib><creatorcontrib>Bonati, Elisa</creatorcontrib><creatorcontrib>Lucchini, Gianluca</creatorcontrib><creatorcontrib>Aiello, Marina</creatorcontrib><creatorcontrib>Santilli, Daniele</creatorcontrib><creatorcontrib>Mozzani, Flavio</creatorcontrib><creatorcontrib>Imberti, Davide</creatorcontrib><creatorcontrib>Michieletti, Emanuele</creatorcontrib><creatorcontrib>Arrigoni, Eugenio</creatorcontrib><creatorcontrib>Delsante, Giovanni</creatorcontrib><creatorcontrib>Pellerito, Raffaele</creatorcontrib><creatorcontrib>Fusaro, Enrico</creatorcontrib><creatorcontrib>Chetta, Alfredo</creatorcontrib><creatorcontrib>Sverzellati, Nicola</creatorcontrib><title>Quantitative chest computed tomography is associated with two prediction models of mortality in interstitial lung disease related to systemic sclerosis</title><title>Rheumatology (Oxford, England)</title><addtitle>Rheumatology (Oxford)</addtitle><description>In this multicentre study, we aimed to evaluate the capacity of a computer-assisted automated QCT method to identify patients with SSc-associated interstitial lung disease (SSc-ILD) with high mortality risk according to validated composite clinical indexes (ILD-Gender, Age, Physiology index and du Bois index).
Chest CT, anamnestic data and pulmonary function tests of 146 patients with SSc were retrospectively collected, and the ILD-Gender, Age, Physiology score and DuBois index were calculated. Each chest CT underwent an operator-independent quantitative assessment performed with a free medical image viewer (Horos). The correlation between clinical prediction models and QCT parameters was tested. A value of P < 0.05 was considered statistically significant.
Most QCT parameters had a statistically different distribution in patients with diverging mortality risk according to both clinical prediction models (P < 0.01). The cut-offs of QCT parameters were calculated by receiver operating characteristic curve analysis, and most of them could discriminate patients with different mortality risk according to clinical prediction models.
QCT assessment of SSc-ILD can discriminate between well-defined different mortality risk categories, supporting its prognostic value. These findings, together with the operator independence, strengthen the validity and clinical usefulness of QCT for assessment of SSc-ILD.</description><subject>Female</subject><subject>Humans</subject><subject>Italy - epidemiology</subject><subject>Lung Diseases, Interstitial - diagnostic imaging</subject><subject>Lung Diseases, Interstitial - mortality</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Models, Statistical</subject><subject>Retrospective Studies</subject><subject>Risk Assessment</subject><subject>Scleroderma, Systemic - diagnostic imaging</subject><subject>Scleroderma, Systemic - mortality</subject><subject>Tomography, X-Ray Computed - mortality</subject><issn>1462-0324</issn><issn>1462-0332</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpNUdtqGzEQFaEhSZ18QaHosS9udNmL_FhC0wQMpZA8L1ppZKvVrrYabY2_JL9bGScmMDAzzDlzZjiEfOLsK2creZu2MA86xxA3-9s_sKsUOyNXvGrEkkkpPpxqUV2Sj4i_GWM1l-qCXArFm9K1V-Tl16zH7LPO_h9QswXM1MRhmjNYmuMQN0lP2z31SDViNF4fBjuftzTvIp0SWG-yjyMdooWANLpSpayDz4U1lsiQsCh4HWiYxw21HkEj0ARBH1Uo7jHD4A1FEyBF9HhNzp0OCDeveUGe778_3T0s1z9_PN59Wy-NrFle9oq3zjVGGN7WDZhaud4K10BfaWt7ZhWrjITGqZVwzCirVpUywmrdCFW3rVyQL8e9U4p_5_J9N3g0EIIeIc7YcdXUtRSsZQUqj1BTLsQErpuSH3Tad5x1B0e69450R0cK6_OrwNwPYE-cNwvkf-Xykjo</recordid><startdate>20170601</startdate><enddate>20170601</enddate><creator>Ariani, Alarico</creator><creator>Silva, Mario</creator><creator>Seletti, Valeria</creator><creator>Bravi, Elena</creator><creator>Saracco, Marta</creator><creator>Parisi, Simone</creator><creator>De Gennaro, Fabio</creator><creator>Idolazzi, Luca</creator><creator>Caramaschi, Paola</creator><creator>Benini, Camilla</creator><creator>Bodini, Flavio Cesare</creator><creator>Scirè, Carlo Alberto</creator><creator>Carrara, Greta</creator><creator>Lumetti, Federica</creator><creator>Alfieri, Veronica</creator><creator>Bonati, Elisa</creator><creator>Lucchini, Gianluca</creator><creator>Aiello, Marina</creator><creator>Santilli, Daniele</creator><creator>Mozzani, Flavio</creator><creator>Imberti, Davide</creator><creator>Michieletti, Emanuele</creator><creator>Arrigoni, Eugenio</creator><creator>Delsante, Giovanni</creator><creator>Pellerito, Raffaele</creator><creator>Fusaro, Enrico</creator><creator>Chetta, Alfredo</creator><creator>Sverzellati, Nicola</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20170601</creationdate><title>Quantitative chest computed tomography is associated with two prediction models of mortality in interstitial lung disease related to systemic sclerosis</title><author>Ariani, Alarico ; 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Chest CT, anamnestic data and pulmonary function tests of 146 patients with SSc were retrospectively collected, and the ILD-Gender, Age, Physiology score and DuBois index were calculated. Each chest CT underwent an operator-independent quantitative assessment performed with a free medical image viewer (Horos). The correlation between clinical prediction models and QCT parameters was tested. A value of P < 0.05 was considered statistically significant.
Most QCT parameters had a statistically different distribution in patients with diverging mortality risk according to both clinical prediction models (P < 0.01). The cut-offs of QCT parameters were calculated by receiver operating characteristic curve analysis, and most of them could discriminate patients with different mortality risk according to clinical prediction models.
QCT assessment of SSc-ILD can discriminate between well-defined different mortality risk categories, supporting its prognostic value. These findings, together with the operator independence, strengthen the validity and clinical usefulness of QCT for assessment of SSc-ILD.</abstract><cop>England</cop><pmid>28160007</pmid><doi>10.1093/rheumatology/kew480</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Female Humans Italy - epidemiology Lung Diseases, Interstitial - diagnostic imaging Lung Diseases, Interstitial - mortality Male Middle Aged Models, Statistical Retrospective Studies Risk Assessment Scleroderma, Systemic - diagnostic imaging Scleroderma, Systemic - mortality Tomography, X-Ray Computed - mortality |
title | Quantitative chest computed tomography is associated with two prediction models of mortality in interstitial lung disease related to systemic sclerosis |
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